class_docstr: Base class for Item-Item Nearest Neighbour recommender models
  here.
  
  Parameters
  ----------
  K : int, optional
      The number of neighbours to include when calculating the item-item
      similarity matrix
  num_threads : int, optional
      The number of threads to use for fitting the model. Specifying 0
      means to default to the number of cores on the machine.
code: def similar_items(
      self,
      itemid,
      N=10,
      recalculate_item=False,
      item_users=None,
      filter_items=None,
      items=None,
  ):
      if recalculate_item:
          raise NotImplementedError(
              "Recalculate_item isn't implemented"
          )
  
      if not np.isscalar(itemid):
          return _batch_call(
              self.similar_items,
              itemid,
              N=N,
              filter_items=filter_items,
              items=items,
          )
  
      if filter_items is not None and items is not None:
          raise ValueError("Can't specify both filter_items and items")
  
      if itemid >= self.similarity.shape[0]:
          return np.array([]), np.array([])
  
      ids = self.similarity[itemid].indices
      scores = self.similarity[itemid].data
  
      if filter_items is not None:
          mask = np.in1d(ids, filter_items, invert=True)
          ids, scores = ids[mask], scores[mask]
  
      elif items is not None:
          mask = np.in1d(ids, items)
          ids, scores = ids[mask], scores[mask]
  
          # returned items should be equal to input selected items
          missing = items[np.in1d(items, ids, invert=True)]
          if missing.size:
              ids = np.append(ids, missing)
              scores = np.append(
                  scores,
                  np.full(missing.size, -np.finfo(scores.dtype).max),
              )
  
      best = np.argsort(scores)[::-1][:N]
      return ids[best], scores[best]
  
imports: import numpy
  from numpy import bincount, log, log1p, sqrt
  from scipy.sparse import coo_matrix, csr_matrix
  from ._nearest_neighbours import NearestNeighboursScorer, all_pairs_knn
  from .recommender_base import RecommenderBase
  from .utils import _batch_call
label_desc: Use image node_id 5 for calling the ItemItemRecommender obj's underlying member method code with example usage: obj.similar_items(itemid, N, recalculate_item, item_users, filter_items, items) and returns: ids,  scores, _batch_call, np,  np